---
title: "Discussion on \"DeepSeek-V4.1-Flash-1017 Compresses Agent Trajectories: What Open Weights Mean for Campus Compute Budgets\" | Hashnode"
description: "Discussion on \"DeepSeek-V4.1-Flash-1017 Compresses Agent Trajectories: What Open Weights Mean for Campus Compute Budgets\". Reinforcement learning with self-feedback cuts agent token bloat by 28%, enabling predictable campus compute budgets."
url: "https://hashnode.com/posts/deepseek-v4-1-flash-1017-compresses-agent-trajectories-what-open-weights-mean-for-campus-compute-budgets/6ad413aa88c4c7a7463dac96"
---

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# [DeepSeek-V4.1-Flash-1017 Compresses Agent Trajectories: What Open Weights Mean for Campus Compute Budgets](https://blog.edtechniti.com/deepseek-v4-1-flash-1017-compresses-agent-trajectories-what-open-weights-mean-for-campus-compute-budgets?utm_source=hashnode&utm_medium=feed)

Reinforcement learning with self-feedback cuts agent token bloat by 28%, enabling predictable campus compute budgets.

Oct 18 · 6 min read · blog.edtechniti.com  
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[Nash Hasan](https://hashnode.com/@edtechniti)

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## Responses 1

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[Yunus Emre Vurgun](https://hashnode.com/@yunusemrevurgun)

[Oct 18](https://hashnode.com/posts/deepseek-v4-1-flash-1017-compresses-agent-trajectories-what-open-weights-mean-for-campus-compute-budgets/6ad413aa88c4c7a7463dac96/comment/6ad458b314ee79bfe979a7da)

Worth adding for anyone budgeting from this: fewer generated tokens per agent task doesn't always mean lower cost, because in multi-step runs the input side (the growing context resent every step) often costs more than the output. Before switching, I'd measure input tokens, output tokens and number of steps on a few of your real workloads. Also check the task success rate, since a shorter run that fails more often ends up costing more.